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Unsupervised pattern discovery in au...

Noering, Fabian Kai Dietrich.

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Unsupervised pattern discovery in automotive time series = pattern-based construction of representative driving cycles /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Unsupervised pattern discovery in automotive time series/ by Fabian Kai Dietrich Noering.
Reminder of title:	pattern-based construction of representative driving cycles /
Author:	Noering, Fabian Kai Dietrich.
Published:	Wiesbaden :Springer Fachmedien Wiesbaden : : 2022.,
Description:	xxi, 148 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Introduction -- RelatedWork -- Development of Pattern Discovery Algorithms for Automotive Time Series -- Pattern-based Representative Cycles -- Evaluation -- Conclusion.
Contained By:	Springer Nature eBook
Subject:	Motor vehicle driving - Mathematical models. -
Online resource:	https://doi.org/10.1007/978-3-658-36336-9
ISBN:	9783658363369

Unsupervised pattern discovery in automotive time series = pattern-based construction of representative driving cycles /
Noering, Fabian Kai Dietrich.

Unsupervised pattern discovery in automotive time seriespattern-based construction of representative driving cycles /[electronic resource] :by Fabian Kai Dietrich Noering. - Wiesbaden :Springer Fachmedien Wiesbaden :2022. - xxi, 148 p. :ill. (some col.), digital ;24 cm. - Autouni - Schriftenreihe,v. 1592512-1154 ;. - Autouni - Schriftenreihe ;v. 159..

Introduction -- RelatedWork -- Development of Pattern Discovery Algorithms for Automotive Time Series -- Pattern-based Representative Cycles -- Evaluation -- Conclusion.

In the last decade unsupervised pattern discovery in time series, i.e. the problem of finding recurrent similar subsequences in long multivariate time series without the need of querying subsequences, has earned more and more attention in research and industry. Pattern discovery was already successfully applied to various areas like seismology, medicine, robotics or music. Until now an application to automotive time series has not been investigated. This dissertation fills this desideratum by studying the special characteristics of vehicle sensor logs and proposing an appropriate approach for pattern discovery. To prove the benefit of pattern discovery methods in automotive applications, the algorithm is applied to construct representative driving cycles. About the author Fabian Kai Dietrich Noering is currently working in the technical development of Volkswagen AG as data scientist with a special interest in the analysis of time series regarding e.g. product optimization.

ISBN: 9783658363369

Standard No.: 10.1007/978-3-658-36336-9doiSubjects--Topical Terms:

3595771
Motor vehicle driving
--Mathematical models.

LC Class. No.: TL152.5 / .N64 2022

Dewey Class. No.: 629.283

Unsupervised pattern discovery in automotive time series = pattern-based construction of representative driving cycles /
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505 0 $a Introduction -- RelatedWork -- Development of Pattern Discovery Algorithms for Automotive Time Series -- Pattern-based Representative Cycles -- Evaluation -- Conclusion.
520 $a In the last decade unsupervised pattern discovery in time series, i.e. the problem of finding recurrent similar subsequences in long multivariate time series without the need of querying subsequences, has earned more and more attention in research and industry. Pattern discovery was already successfully applied to various areas like seismology, medicine, robotics or music. Until now an application to automotive time series has not been investigated. This dissertation fills this desideratum by studying the special characteristics of vehicle sensor logs and proposing an appropriate approach for pattern discovery. To prove the benefit of pattern discovery methods in automotive applications, the algorithm is applied to construct representative driving cycles. About the author Fabian Kai Dietrich Noering is currently working in the technical development of Volkswagen AG as data scientist with a special interest in the analysis of time series regarding e.g. product optimization.
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